An evaluation of task performance capabilities in a network-centric system with neural networks usage
The study considers the problem touching upon analyzing the military unit capabilities. The ability to accurately assess and predict the functional potential of the units is outstanding for effective strategic and operational planning. Established methodologies use strict calculation algorithm and m...
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| Datum: | 2025 |
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Інститут проблем реєстрації інформації НАН України
2025
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| Online Zugang: | https://drsp.ipri.kiev.ua/article/view/354610 |
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| Назва журналу: | Data Recording, Storage & Processing |
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Data Recording, Storage & Processing| _version_ | 1862315021594263552 |
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| author | Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. |
| author_facet | Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. |
| author_sort | Бойченко, А. В. |
| baseUrl_str | http://drsp.ipri.kiev.ua/oai |
| collection | OJS |
| datestamp_date | 2026-04-12T15:36:33Z |
| description | The study considers the problem touching upon analyzing the military unit capabilities. The ability to accurately assess and predict the functional potential of the units is outstanding for effective strategic and operational planning. Established methodologies use strict calculation algorithm and mandatory formulas. Their limitation is an inflexibility during the crucial stages of result verification and validation. To overcome these constraints, a neural network–based framework for capability assessment is proposed. A key novelty is providing a continuous scale of assessment, offering a more nuanced view than traditional classifications. The formation of a training dataset can be carried out using two approaches. The first approach relies on human expert evaluation, where a specialist determines the capability level of a unit based on a predefined scale. The second approach involves the automated generation of labels using formulas, where a specific importance coefficient is assigned to each parameter. Within the suggested distributed architecture, each neural networks handle capability evaluation levels, ensuring modularity, scalability, and system resilience. To build the system can be used open software libraries for machine learning, data collection, and analysis. The approach simplifies the update process for individual system nodes. Input and output parameters can be changed or edited without the need to retrain the network. The results can be deployed into analytical support systems for military commanders and staff at all levels of command and control. The implementation of the system contributes directly to mission success and force preservation by reducing the probability of assigning unattainable or impossible tasks to subordinate units. Fig.: 5. Refs: 17 titles. |
| doi_str_mv | 10.35681/1560-9189.2025.27.3.354610 |
| first_indexed | 2026-04-13T01:00:05Z |
| format | Article |
| id | drspiprikievua-article-354610 |
| institution | Data Recording, Storage & Processing |
| keywords_txt_mv | keywords |
| language | Ukrainian |
| last_indexed | 2026-04-13T01:00:05Z |
| publishDate | 2025 |
| publisher | Інститут проблем реєстрації інформації НАН України |
| record_format | ojs |
| spelling | drspiprikievua-article-3546102026-04-12T15:36:33Z An evaluation of task performance capabilities in a network-centric system with neural networks usage Оцінка спроможностей виконання завдань у мережево-центричній системі із використанням нейронних мереж Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. neural network, capability, opportunity, artificial intelligence нейромережа, спроможність, можливість, штучний інтелект The study considers the problem touching upon analyzing the military unit capabilities. The ability to accurately assess and predict the functional potential of the units is outstanding for effective strategic and operational planning. Established methodologies use strict calculation algorithm and mandatory formulas. Their limitation is an inflexibility during the crucial stages of result verification and validation. To overcome these constraints, a neural network–based framework for capability assessment is proposed. A key novelty is providing a continuous scale of assessment, offering a more nuanced view than traditional classifications. The formation of a training dataset can be carried out using two approaches. The first approach relies on human expert evaluation, where a specialist determines the capability level of a unit based on a predefined scale. The second approach involves the automated generation of labels using formulas, where a specific importance coefficient is assigned to each parameter. Within the suggested distributed architecture, each neural networks handle capability evaluation levels, ensuring modularity, scalability, and system resilience. To build the system can be used open software libraries for machine learning, data collection, and analysis. The approach simplifies the update process for individual system nodes. Input and output parameters can be changed or edited without the need to retrain the network. The results can be deployed into analytical support systems for military commanders and staff at all levels of command and control. The implementation of the system contributes directly to mission success and force preservation by reducing the probability of assigning unattainable or impossible tasks to subordinate units. Fig.: 5. Refs: 17 titles. Розглянуто питання оцінки спроможностей військових підрозділів, що є надзвичайно актуальним у сучасних умовах. Показано що формульні методики оцінювання потребують удосконалення, зокрема уточнення результатів та підвищення швидкості розрахунків особливо у випадках, коли розраховане значення знаходиться на межі між категоріями значень спроможностей. Запропонована система з використанням нейронних мереж для оцінки спроможностей, яка дозволить значно підвищити точність, об’єктивність та оперативність розрахунку спроможностей і можливостей військових підрозділів. Результати можуть бути інтегровані у системи військової аналітики, що покращить ситуаційну обізнаність командування та дозволить уникати свідомо нездійсненних завдань. Інститут проблем реєстрації інформації НАН України 2025-12-23 Article Article application/pdf https://drsp.ipri.kiev.ua/article/view/354610 10.35681/1560-9189.2025.27.3.354610 Data Recording, Storage & Processing; Vol. 27 No. 3 (2025); 98-111 Регистрация, хранение и обработка данных; Том 27 № 3 (2025); 98-111 Реєстрація, зберігання і обробка даних; Том 27 № 3 (2025); 98-111 1560-9189 uk https://drsp.ipri.kiev.ua/article/view/354610/343189 Авторське право (c) 2025 Реєстрація, зберігання і обробка даних |
| spellingShingle | neural network capability opportunity artificial intelligence Бойченко, А. В. Додонов, В. О. Залужний, В. Ф. Ізварін, Є. І. An evaluation of task performance capabilities in a network-centric system with neural networks usage |
| title | An evaluation of task performance capabilities in a network-centric system with neural networks usage |
| title_alt | Оцінка спроможностей виконання завдань у мережево-центричній системі із використанням нейронних мереж |
| title_full | An evaluation of task performance capabilities in a network-centric system with neural networks usage |
| title_fullStr | An evaluation of task performance capabilities in a network-centric system with neural networks usage |
| title_full_unstemmed | An evaluation of task performance capabilities in a network-centric system with neural networks usage |
| title_short | An evaluation of task performance capabilities in a network-centric system with neural networks usage |
| title_sort | evaluation of task performance capabilities in a network-centric system with neural networks usage |
| topic | neural network capability opportunity artificial intelligence |
| topic_facet | neural network capability opportunity artificial intelligence нейромережа спроможність можливість штучний інтелект |
| url | https://drsp.ipri.kiev.ua/article/view/354610 |
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